{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from tqdm import tqdm\n",
    "from sklearn.model_selection import StratifiedKFold\n",
    "import tensorflow as tf\n",
    "import tensorflow.keras.backend as K\n",
    "from tensorflow.keras.utils import to_categorical\n",
    "from transformers import BertTokenizer,BertConfig,TFBertModel\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "%pylab inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_df = pd.read_csv('../data/train_set.csv', sep='\\t', nrows=7000)\n",
    "test_df = pd.read_csv('../data/test_a.csv', sep='\\t')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>label</th>\n",
       "      <th>text</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2</td>\n",
       "      <td>2967 6758 339 2021 1854 3731 4109 3792 4149 15...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>11</td>\n",
       "      <td>4464 486 6352 5619 2465 4802 1452 3137 5778 54...</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>7346 4068 5074 3747 5681 6093 1777 2226 7354 6...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>7159 948 4866 2109 5520 2490 211 3956 5520 549...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3</td>\n",
       "      <td>3646 3055 3055 2490 4659 6065 3370 5814 2465 5...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   label                                               text\n",
       "0      2  2967 6758 339 2021 1854 3731 4109 3792 4149 15...\n",
       "1     11  4464 486 6352 5619 2465 4802 1452 3137 5778 54...\n",
       "2      3  7346 4068 5074 3747 5681 6093 1777 2226 7354 6...\n",
       "3      2  7159 948 4866 2109 5520 2490 211 3956 5520 549...\n",
       "4      3  3646 3055 3055 2490 4659 6065 3370 5814 2465 5..."
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Calling BertTokenizer.from_pretrained() with the path to a single file or url is deprecated\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'input_ids': [2, 5612, 1106, 1529, 518, 3], 'token_type_ids': [0, 0, 0, 0, 0, 0], 'attention_mask': [1, 1, 1, 1, 1, 1]}"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tokenizer = BertTokenizer.from_pretrained('../emb/bert_base_chinese/vocab.txt')\n",
    "tokenizer.encode_plus(\"2960 6758 339 2021\",\n",
    "        add_special_tokens=True,\n",
    "        max_length=20,\n",
    "        truncation=True)\n",
    "# token_type_ids 通常第一个句子全部标记为0，第二个句子全部标记为1。\n",
    "# attention_mask padding的地方为0，未padding的地方为1。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def _convert_to_transformer_inputs(instance, tokenizer, max_sequence_length):\n",
    "    \"\"\"Converts tokenized input to ids, masks and segments for transformer (including bert)\"\"\"\n",
    "    \"\"\"默认返回input_ids,token_type_ids,attention_mask\"\"\"\n",
    "    inputs = tokenizer.encode_plus(instance,\n",
    "        add_special_tokens=True,\n",
    "        max_length=max_sequence_length,\n",
    "        truncation=True)\n",
    "\n",
    "    input_ids =  inputs[\"input_ids\"]\n",
    "    input_masks = inputs[\"attention_mask\"]\n",
    "    input_segments = inputs[\"token_type_ids\"]\n",
    "    padding_length = max_sequence_length - len(input_ids)\n",
    "    # 填充\n",
    "    padding_id = tokenizer.pad_token_id\n",
    "    input_ids = input_ids + ([padding_id] * padding_length)\n",
    "    input_masks = input_masks + ([0] * padding_length)\n",
    "    input_segments = input_segments + ([0] * padding_length)\n",
    "    return [input_ids, input_masks, input_segments]\n",
    "\n",
    "\n",
    "def compute_input_arrays(df, columns, tokenizer, max_sequence_length):\n",
    "    input_ids, input_masks, input_segments = [], [], []\n",
    "    for instance in tqdm(df[columns]):\n",
    "        \n",
    "        ids, masks, segments = _convert_to_transformer_inputs(str(instance), tokenizer, max_sequence_length)\n",
    "        \n",
    "        input_ids.append(ids)\n",
    "        input_masks.append(masks)\n",
    "        input_segments.append(segments)\n",
    "\n",
    "    return [np.asarray(input_ids, dtype=np.int32), \n",
    "            np.asarray(input_masks, dtype=np.int32), \n",
    "            np.asarray(input_segments, dtype=np.int32)\n",
    "           ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 7000/7000 [01:51<00:00, 63.03it/s]\n",
      "100%|██████████| 50000/50000 [13:13<00:00, 63.01it/s]  \n"
     ]
    }
   ],
   "source": [
    "MAX_SEQUENCE_LENGTH = 128\n",
    "input_categories = 'text'\n",
    "inputs = compute_input_arrays(train_df, input_categories, tokenizer, MAX_SEQUENCE_LENGTH)\n",
    "test_inputs = compute_input_arrays(test_df, input_categories, tokenizer, MAX_SEQUENCE_LENGTH)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 标签类别转换"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "output_categories = 'label'\n",
    "def compute_output_arrays(df, columns):\n",
    "    return np.asarray(df[columns].astype(int))\n",
    "outputs = compute_output_arrays(train_df, output_categories)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# BERT模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def Focal_Loss(y_true, y_pred, alpha=0.5, gamma=2):\n",
    "    \"\"\"\n",
    "    focal loss for multi-class classification\n",
    "    fl(pt) = -alpha*(1-pt)^(gamma)*log(pt)\n",
    "    :param y_true: ground truth one-hot vector shape of [batch_size, nb_class]\n",
    "    :param y_pred: prediction after softmax shape of [batch_size, nb_class]\n",
    "    :param alpha:\n",
    "    :param gamma:\n",
    "    :return:\n",
    "    \"\"\"\n",
    "    y_pred += tf.keras.backend.epsilon()\n",
    "    ce = -y_true * tf.math.log(y_pred)\n",
    "    weight = tf.pow(1 - y_pred, gamma) * y_true\n",
    "    fl = ce * weight * alpha\n",
    "    reduce_fl = tf.keras.backend.max(fl, axis=-1)\n",
    "    return reduce_fl"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_model():\n",
    "    input_id = tf.keras.layers.Input((MAX_SEQUENCE_LENGTH,), dtype=tf.int32)    \n",
    "    input_mask = tf.keras.layers.Input((MAX_SEQUENCE_LENGTH,), dtype=tf.int32)    \n",
    "    input_atn = tf.keras.layers.Input((MAX_SEQUENCE_LENGTH,), dtype=tf.int32)\n",
    "    \n",
    "    config = BertConfig.from_pretrained('../emb/bert_base_chinese/bert-base-chinese-config.json', output_hidden_states=True)\n",
    "    bert_model = TFBertModel.from_pretrained('../emb/bert_base_chinese/bert-base-chinese-tf_model.h5', config=config)\n",
    "    \n",
    "    sequence_output, pooler_output, hidden_states = bert_model(input_id, attention_mask=input_mask, token_type_ids=input_atn)\n",
    "    # (bs,140,768)(bs,768)\n",
    "    x = tf.keras.layers.GlobalAveragePooling1D()(sequence_output)    \n",
    "    x = tf.keras.layers.Dropout(0.15)(x)\n",
    "    x = tf.keras.layers.Dense(14, activation='softmax')(x)\n",
    "\n",
    "    model = tf.keras.models.Model(inputs=[input_id, input_mask, input_atn], outputs=x)\n",
    "    optimizer = tf.keras.optimizers.Nadam(learning_rate=1e-5)\n",
    "    FL = lambda y_true,y_pred: Focal_Loss(y_true, y_pred, alpha=0.25, gamma=2)\n",
    "    model.compile(loss=FL, optimizer=optimizer, metrics=['acc'])\n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "def history_look(history):\n",
    "    history_dict = history.history\n",
    "\n",
    "    acc = history_dict['acc']\n",
    "    val_acc = history_dict['val_acc']\n",
    "    loss=history_dict['loss']\n",
    "    val_loss=history_dict['val_loss']\n",
    "\n",
    "    epochs = range(1, len(acc) + 1)\n",
    "\n",
    "    plt.figure(figsize=(6,4))\n",
    "    plt.plot(epochs, loss, 'bo', label='Training loss')\n",
    "    plt.plot(epochs, val_loss, 'b', label='Validation loss')\n",
    "    plt.title('Training and validation loss')\n",
    "    plt.xlabel('Epochs')\n",
    "    plt.ylabel('Loss')\n",
    "    plt.legend()\n",
    "    plt.show()\n",
    "\n",
    "    plt.figure(figsize=(6,4))\n",
    "    plt.plot(epochs, acc, 'bo', label='Training acc')\n",
    "    plt.plot(epochs, val_acc, 'b', label='Validation acc')\n",
    "    plt.title('Training and validation accuracy')\n",
    "    plt.xlabel('Epochs')\n",
    "    plt.ylabel('Accuracy')\n",
    "    plt.legend(loc='lower right')\n",
    "    plt.ylim((0.5,1))\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 5600 samples, validate on 1400 samples\n",
      "Epoch 1/7\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['tf_bert_model/bert/pooler/dense/kernel:0', 'tf_bert_model/bert/pooler/dense/bias:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['tf_bert_model/bert/pooler/dense/kernel:0', 'tf_bert_model/bert/pooler/dense/bias:0'] when minimizing the loss.\n",
      "5600/5600 [==============================] - 268s 48ms/sample - loss: 0.2905 - acc: 0.5082 - val_loss: 0.1589 - val_acc: 0.7250\n",
      "Epoch 2/7\n",
      "5600/5600 [==============================] - 245s 44ms/sample - loss: 0.1219 - acc: 0.7645 - val_loss: 0.1327 - val_acc: 0.7486\n",
      "Epoch 3/7\n",
      "5600/5600 [==============================] - 246s 44ms/sample - loss: 0.0730 - acc: 0.8332 - val_loss: 0.1157 - val_acc: 0.7814\n",
      "Epoch 4/7\n",
      "5600/5600 [==============================] - 246s 44ms/sample - loss: 0.0456 - acc: 0.8863 - val_loss: 0.0929 - val_acc: 0.8257\n",
      "Epoch 5/7\n",
      "5600/5600 [==============================] - 246s 44ms/sample - loss: 0.0273 - acc: 0.9220 - val_loss: 0.0996 - val_acc: 0.8286\n",
      "Epoch 6/7\n",
      "5600/5600 [==============================] - 247s 44ms/sample - loss: 0.0171 - acc: 0.9480 - val_loss: 0.0954 - val_acc: 0.8507\n",
      "Epoch 7/7\n",
      "5600/5600 [==============================] - 247s 44ms/sample - loss: 0.0145 - acc: 0.9530 - val_loss: 0.1103 - val_acc: 0.8264\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 5600 samples, validate on 1400 samples\n",
      "Epoch 1/7\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['tf_bert_model/bert/pooler/dense/kernel:0', 'tf_bert_model/bert/pooler/dense/bias:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['tf_bert_model/bert/pooler/dense/kernel:0', 'tf_bert_model/bert/pooler/dense/bias:0'] when minimizing the loss.\n",
      "5600/5600 [==============================] - 270s 48ms/sample - loss: 0.2842 - acc: 0.5121 - val_loss: 0.1896 - val_acc: 0.6457\n",
      "Epoch 2/7\n",
      "5600/5600 [==============================] - 245s 44ms/sample - loss: 0.1182 - acc: 0.7589 - val_loss: 0.1389 - val_acc: 0.7571\n",
      "Epoch 3/7\n",
      "5600/5600 [==============================] - 245s 44ms/sample - loss: 0.0704 - acc: 0.8425 - val_loss: 0.1084 - val_acc: 0.8079\n",
      "Epoch 4/7\n",
      "5600/5600 [==============================] - 244s 44ms/sample - loss: 0.0414 - acc: 0.8950 - val_loss: 0.1209 - val_acc: 0.7993\n",
      "Epoch 5/7\n",
      "5600/5600 [==============================] - 244s 44ms/sample - loss: 0.0287 - acc: 0.9207 - val_loss: 0.1250 - val_acc: 0.8071\n",
      "Epoch 6/7\n",
      "5600/5600 [==============================] - 244s 44ms/sample - loss: 0.0196 - acc: 0.9425 - val_loss: 0.1155 - val_acc: 0.8214\n",
      "Epoch 7/7\n",
      "5600/5600 [==============================] - 244s 44ms/sample - loss: 0.0165 - acc: 0.9496 - val_loss: 0.1190 - val_acc: 0.8221\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 5600 samples, validate on 1400 samples\n",
      "Epoch 1/7\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['tf_bert_model/bert/pooler/dense/kernel:0', 'tf_bert_model/bert/pooler/dense/bias:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['tf_bert_model/bert/pooler/dense/kernel:0', 'tf_bert_model/bert/pooler/dense/bias:0'] when minimizing the loss.\n",
      "5600/5600 [==============================] - 269s 48ms/sample - loss: 0.2947 - acc: 0.5041 - val_loss: 0.1538 - val_acc: 0.6986\n",
      "Epoch 2/7\n",
      "5600/5600 [==============================] - 243s 43ms/sample - loss: 0.1301 - acc: 0.7513 - val_loss: 0.1357 - val_acc: 0.7443\n",
      "Epoch 3/7\n",
      "5600/5600 [==============================] - 244s 43ms/sample - loss: 0.0743 - acc: 0.8389 - val_loss: 0.0998 - val_acc: 0.8029\n",
      "Epoch 4/7\n",
      "5600/5600 [==============================] - 244s 44ms/sample - loss: 0.0470 - acc: 0.8830 - val_loss: 0.0949 - val_acc: 0.8293\n",
      "Epoch 5/7\n",
      "5600/5600 [==============================] - 244s 44ms/sample - loss: 0.0279 - acc: 0.9184 - val_loss: 0.0997 - val_acc: 0.8350\n",
      "Epoch 6/7\n",
      "5600/5600 [==============================] - 244s 44ms/sample - loss: 0.0173 - acc: 0.9470 - val_loss: 0.1172 - val_acc: 0.8329\n",
      "Epoch 7/7\n",
      "5600/5600 [==============================] - 244s 44ms/sample - loss: 0.0147 - acc: 0.9530 - val_loss: 0.1360 - val_acc: 0.8164\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 5600 samples, validate on 1400 samples\n",
      "Epoch 1/7\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['tf_bert_model/bert/pooler/dense/kernel:0', 'tf_bert_model/bert/pooler/dense/bias:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['tf_bert_model/bert/pooler/dense/kernel:0', 'tf_bert_model/bert/pooler/dense/bias:0'] when minimizing the loss.\n",
      "5600/5600 [==============================] - 268s 48ms/sample - loss: 0.3133 - acc: 0.4841 - val_loss: 0.1663 - val_acc: 0.6979\n",
      "Epoch 2/7\n",
      "5600/5600 [==============================] - 243s 43ms/sample - loss: 0.1318 - acc: 0.7500 - val_loss: 0.1066 - val_acc: 0.7843\n",
      "Epoch 3/7\n",
      "5600/5600 [==============================] - 243s 43ms/sample - loss: 0.0808 - acc: 0.8341 - val_loss: 0.1381 - val_acc: 0.7621\n",
      "Epoch 4/7\n",
      "5600/5600 [==============================] - 242s 43ms/sample - loss: 0.0503 - acc: 0.8820 - val_loss: 0.0977 - val_acc: 0.8114\n",
      "Epoch 5/7\n",
      "5600/5600 [==============================] - 242s 43ms/sample - loss: 0.0302 - acc: 0.9146 - val_loss: 0.0947 - val_acc: 0.8286\n",
      "Epoch 6/7\n",
      "5600/5600 [==============================] - 242s 43ms/sample - loss: 0.0175 - acc: 0.9473 - val_loss: 0.1036 - val_acc: 0.8307\n",
      "Epoch 7/7\n",
      "5600/5600 [==============================] - 242s 43ms/sample - loss: 0.0125 - acc: 0.9589 - val_loss: 0.1027 - val_acc: 0.8364\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 5600 samples, validate on 1400 samples\n",
      "Epoch 1/7\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['tf_bert_model/bert/pooler/dense/kernel:0', 'tf_bert_model/bert/pooler/dense/bias:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['tf_bert_model/bert/pooler/dense/kernel:0', 'tf_bert_model/bert/pooler/dense/bias:0'] when minimizing the loss.\n",
      "5600/5600 [==============================] - 268s 48ms/sample - loss: 0.4813 - acc: 0.2023 - val_loss: 0.4645 - val_acc: 0.1593\n",
      "Epoch 2/7\n",
      "5600/5600 [==============================] - 242s 43ms/sample - loss: 0.4327 - acc: 0.2695 - val_loss: 0.2453 - val_acc: 0.5750\n",
      "Epoch 3/7\n",
      "5600/5600 [==============================] - 242s 43ms/sample - loss: 0.1689 - acc: 0.6934 - val_loss: 0.1196 - val_acc: 0.7721\n",
      "Epoch 4/7\n",
      "5600/5600 [==============================] - 243s 43ms/sample - loss: 0.0902 - acc: 0.8152 - val_loss: 0.1007 - val_acc: 0.8086\n",
      "Epoch 5/7\n",
      "5600/5600 [==============================] - 243s 43ms/sample - loss: 0.0545 - acc: 0.8777 - val_loss: 0.1053 - val_acc: 0.8100\n",
      "Epoch 6/7\n",
      "5600/5600 [==============================] - 243s 43ms/sample - loss: 0.0373 - acc: 0.9080 - val_loss: 0.1088 - val_acc: 0.8157\n",
      "Epoch 7/7\n",
      "5600/5600 [==============================] - 242s 43ms/sample - loss: 0.0207 - acc: 0.9411 - val_loss: 0.1029 - val_acc: 0.8243\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "gkf = StratifiedKFold(n_splits=5).split(X=train_df[input_categories].fillna('-1'), y=train_df[output_categories].fillna('-1'))\n",
    "\n",
    "valid_preds = []\n",
    "test_preds = []\n",
    "for fold, (train_idx, valid_idx) in enumerate(gkf):\n",
    "    train_inputs = [inputs[i][train_idx] for i in range(len(inputs))]\n",
    "    train_outputs = to_categorical(outputs[train_idx])\n",
    "\n",
    "    valid_inputs = [inputs[i][valid_idx] for i in range(len(inputs))]\n",
    "    valid_outputs = to_categorical(outputs[valid_idx])\n",
    "\n",
    "    K.clear_session()  # 销毁当前的TF图并创建一个新图，有助于避免旧模型/图层混乱。\n",
    "    model = create_model()\n",
    "    history = model.fit(train_inputs, train_outputs, validation_data= [valid_inputs, valid_outputs], epochs=7, batch_size=6)\n",
    "    history_look(history) \n",
    "    # model.save_weights(f'bert-{fold}.h5')\n",
    "    valid_preds.append(model.predict(valid_inputs))\n",
    "    test_preds.append(model.predict(test_inputs))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "preds = np.average(test_preds, axis=0)\n",
    "preds = np.argmax(preds,axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "submission = pd.read_csv('../data/test_a_sample_submit.csv')\n",
    "submission['label'] = preds\n",
    "submission.to_csv('../output/BERT_submission.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
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